Statement of Contribution

There are two steps of the shared group strategy: each member should try to solve the problems for themselves, potential discussions are encouraged to inspire learning process; the report is generated and merged in principle presenting the more appropriate solutions out of members’. Generally, both members are capable of delivering their own solutions and equally contributed in the group report.

The same strategy was implemented in report revision, where discussion between members and with classmates comprised the main part of problem identification. In this report, Jun Li has major responsibility for assignment 1 and problem 1-7 in assignment 2, while Fahed Maqbool for problem 8 in assignment.

Assignment 1:

new tree

Assignment 2:

part 3

The graph shows that the hospitals with infection risk of around 4.5 have largest density, and there are sex hospitals which have abnormal values in this dimension.

part 4

All variables have highest probability somewhere in the middle of the range, except medical school affiliation which is discrete variable. Meanwhile, there are no abnormal observations for variables available facilities and services.

part 5

Infection risk and number of nurses seem to share slightly positive correlation.It seems number of beds has positive correlation with number of nurses but not obvious correlation with infection risk. Grouping in color should be a good tool to identify relations between variables, however, in this case “Number of beds” is a continuous variable which is prone to make human eyes tired and draw a misleading conclusion, since this color scale is hard for human being to differentiate between hues.

part 6

This produces a standalone HTML, which has some built-in interactivities and can also be directly published on web. The new interactive plot shows that the mode has density of around 0.38 and infection risk of around 4.4

part 7

part 8

The larger the bandwidth is, the smoother the density plot is. This report suggest around 0.8 as a shared bandwidth for all plots.

Shiny applications not supported in static R Markdown documents

Appendix code

knitr::opts_chunk$set(eval=TRUE,echo=F,
                      message = F, warning = F, error = F,
                      fig.width=7, fig.height=5, fig.align="center")
RNGversion('3.5.1')
library(ggplot2)
library(gridExtra)
library(plotly)
library(shiny)
## part 1.1
da<-read.table("SENIC.txt",header=FALSE)
## part 1.2 
myfunc<-function(x){
  q1<-quantile(x,0.25)
  q3<-quantile(x,0.75)
  h<-as.numeric(q3+1.5*(q3-q1))
  l<-as.numeric(q1-1.5*(q3-q1))
  
  return (c(which(x<l),which(x>h)))
}
## part 1.3
infection_risk<-as.vector(as.matrix(da[4])) ## vector for Infection Risk
ind_out_ir<-myfunc(infection_risk)             ## index of outlier
dens_ir=density(infection_risk)$y  ## density of outlier
shap=rep(1,113)
shap[ind_out_ir]=23  ## 23 symbolisize diamond
da1<-as.data.frame(cbind(infection_risk,dens_ir,shap))

g_part3<-ggplot()+
   geom_density(data=da1,alpha=0.8,mapping=aes(x=infection_risk))+
   scale_shape_identity()+
   geom_point(data=da1[ind_out_ir,],mapping=aes(x=infection_risk,y=0,shape=shap,fill="red"))+
   ggtitle("Density of Infection Risk")+
   theme(plot.title = element_text(hjust = 0.5),legend.position="none")
g_part3
## part 1.4
nvar=ncol(da)
var_name<-c("Identification Number","Length of Stay","Age","Infection Risk","Routine Culturing Ratio","Routine Chest X-ray Ratio","Number of Beds","Medical School Affiliation","Region","Average Daily Census","Number of Nurses","Available Facilities & Services")

gr<-NULL
for(i in c(2:12)[-8]){
  var<-as.vector(as.matrix(da[i])) ## vector for variables
  ind_out<-myfunc(var)             ## index of outlier
  dens=density(var)$y  ## density of outlier
  shap=rep(1,113)
  shap[ind_out]=23  ## 23 symbolisize diamond
  danew<-as.data.frame(cbind(var,dens,shap))

  g<-ggplot()+
     geom_density(data=danew,alpha=0.8,mapping=aes(x=var))+
     ggtitle(paste("Density of",var_name[i]))+
     theme(plot.title = element_text(hjust = 0.5,size=8),legend.position="none")
  if(length(ind_out)!=0) 
     g<-g+
        scale_shape_identity()+
        geom_point(data=danew[ind_out,],mapping=aes(x=var,y=dens,shape=shap,fill="red"))
  gr<-append(gr,list(g))
  #assign(paste("g",i,sep=""),g)
}

grid.arrange(arrangeGrob(grobs=gr))
#grid.arrange(g2,g3,g4,g5,g6,g7,g8,g10,g11,g12,ncol=3,widths=rep(50,3),heights=rep(2,4),
#             layout_matrix = rbind(c(1,2,3), c(4,5,6),c(7,8,9),c(10,11,11)))

## part 1.5
names(da)=var_name ## assign variable names to dataframe

ggplot(da,aes(x=da[,4],y=da[,11],color=da[,7]))+
  geom_point()+
  xlab("Infection risk")+
  ylab("Number of nurses")+
  scale_colour_continuous(name="Number\nof Beds")  ## Legend title and lab names

## part 1.6
ggplotly(g_part3)

## part 1.7
plot<-da %>%  
      plot_ly(x=da[-ind_out_ir,4],type="histogram",name="Infection<br>Risk") %>%
      add_markers(x=da[ind_out_ir,4],y=0,symbol=I(18),name="Outliers") %>%
      layout(title="Histogram of Infection Risk",
             xaxis=list(title="Infection risk"),
             yaxis=list(title="Frequency"))
      
plot
## part 1.8

ui <- fluidPage(
  titlePanel("Show Density Plot!"),
  
  sliderInput(inputId="ws", 
              label="Choose bandwidth size", 
              value=0.01, min=0.1, max=1),
  checkboxGroupInput(inputId="var",
                     label="Choose variables:",
                     choices=c("Length of Stay","Age","Infection Risk","Routine Culturing Ratio","Routine Chest X-ray Ratio","Number of Beds","Medical School Affiliation","Region","Average Daily Census","Number of Nurses","Available Facilities & Services")),
  plotOutput("densPlot")
  
  )

server <- function(input, output) {
  output$densPlot=renderPlot({
    nvar=ncol(da)
    var_name<-c("Identification Number","Length of Stay","Age","Infection Risk","Routine Culturing Ratio","Routine Chest X-ray Ratio","Number of Beds","Medical School Affiliation","Average Daily Census","Number of Nurses","Available Facilities & Services")
    g_all=NULL ## all plot names
    plot_all=NULL ## all plots
    
    len<-length(input$var)
    if(len>0){
      
      ind_selected=NULL  ## index of selected variables
      for(i in 1:len) ind_selected=c(ind_selected,which(input$var[i]==var_name)) 
      #print(input$var)
      #print(ind_selected)
      
      for(i in 1:len){
        var<-as.vector(as.matrix(da[ind_selected[i]])) ## vector for variables
        ind_out<-myfunc(var)             ## index of outlier
        dens=density(var)$y  ## density of outlier
        shap=rep(1,113)
        shap[ind_out]=23  ## 23 symbolisize diamond
        danew<-as.data.frame(cbind(var,dens,shap))
      
        g<-ggplot()+
           geom_density(data=danew,alpha=0.8,mapping=aes(x=var),bw=input$ws)+
           ggtitle(paste("Density of",var_name[ind_selected[i]]))+
           theme(plot.title = element_text(hjust = 0.5,size=8),legend.position="none")
        if(length(ind_out)!=0) 
           g<-g+
              scale_shape_identity()+
              geom_point(data=danew[ind_out,],mapping=aes(x=var,y=dens,shape=shap,fill="red"))
        
        g_now=paste("g",ind_selected[i],sep="") ## current plot name
        g_all=c(g_all,g_now)
        assign(g_now,g)
        plot_all=append(plot_all,list(g))
        }
    }
  
    
    #print("HAHA")
    if(length(g_all)>0) grid.arrange(grobs=plot_all,ncol=3,widths=rep(50,3),heights=rep(2,4))
    }) 
  
}

shinyApp(ui = ui, server = server)